{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本节我们介绍批量归一化（batch normalization）层，它能让较深的神经网络的训练变得更加容易。在3.16节（实战Kaggle比赛：预测房价）里，我们对输入数据做了标准化处理：处理后的任意一个特征在数据集中所有样本上的均值为0、标准差为1。标准化处理输入数据使各个特征的分布相近：这往往更容易训练出有效的模型。\n",
    "\n",
    "通常来说，数据标准化预处理对于浅层模型就足够有效了。随着模型训练的进行，当每层中参数更新时，靠近输出层的输出较难出现剧烈变化。但对深层神经网络来说，即使输入数据已做标准化，训练中模型参数的更新依然很容易造成靠近输出层输出的剧烈变化。这种计算数值的不稳定性通常令我们难以训练出有效的深度模型。\n",
    "\n",
    "批量归一化的提出正是为了应对深度模型训练的挑战。在模型训练时，批量归一化利用小批量上的均值和标准差，不断调整神经网络中间输出，从而使整个神经网络在各层的中间输出的数值更稳定。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 批量归一化层\n",
    "对 全连接层 和 卷积层 做批量归一化的方法稍有不同。以下分别介绍这两种情况下的批量归一化。"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 对全连接层做批量归一化\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 对卷积层做批量归一化\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 预测时的批量归一化\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "import torch.nn.functional as F\n",
    "import sys\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = ('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "# 实现批量归一化\n",
    "def batch_norm(is_training, X, gamma, beta, moving_mean, moving_var, eps, momentum):\n",
    "    # 判断当前模式是训练模式还是预测模式\n",
    "    if not is_training:\n",
    "        # 如果是在预测模式下，直接使用传入的移动平均所得的均值和方差\n",
    "        X_hat = (X - moving_mean) / torch.sqrt(moving_var+eps)\n",
    "    else:\n",
    "        assert len(X.shape) in (2, 4)\n",
    "        # 全连接层的批量归一，对批量样本计算每个特征维上的均值和方差\n",
    "        if len(X.shape) == 2:\n",
    "            mean = X.mean(dim=0) # 相当于按照列来计算\n",
    "            var = ((X-mean)**2).mean(dim=0)\n",
    "        # 卷积层的批量归一，对批量样本计算每个通道维上的均值和方差。保持X的形状以便后面可以做广播运算\n",
    "        else: \n",
    "            mean = X.mean(dim=0, keepdim=True).mean(dim=2, keepdim=True).mean(dim=3, keepdim=True)\n",
    "            var = ((X-mean)**2).mean(dim=0, keepdim=True).mean(dim=2, keepdim=True).mean(dim=3, keepdim=True)\n",
    "        # 训练模式下用当前计算出的均值和方差做标准化\n",
    "        X_hat = (X-mean) / torch.sqrt(var + eps)\n",
    "        # 更新移动平均的均值和方差\n",
    "        moving_mean = momentum * moving_mean + (1.0 - momentum) * mean\n",
    "        moving_var = momentum * moving_var + (1.0 - momentum) * var\n",
    "    Y = gamma * X_hat + beta # 拉伸和偏移\n",
    "    return Y, moving_mean, moving_var\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来，我们自定义一个BatchNorm层。它保存参与求梯度和迭代的拉伸参数gamma和偏移参数beta，同时也维护移动平均得到的均值和方差，以便能够在模型预测时被使用。BatchNorm实例所需指定的num_features参数对于全连接层来说应为输出个数，对于卷积层来说则为输出通道数。该实例所需指定的num_dims参数对于全连接层和卷积层来说分别为2和4。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 实现批量归一化层\n",
    "class BatchNorm(nn.Module):\n",
    "    def __init__(self, num_features, num_dims):\n",
    "        super(BatchNorm, self).__init__()\n",
    "        if num_dims == 2:\n",
    "            shape = (1, num_features)\n",
    "        else:\n",
    "            shape = (1, num_features, 1, 1)\n",
    "        # 参与求梯度和迭代的拉伸和偏移参数，分别初始化成1和0\n",
    "        self.gamma =  nn.Parameter(torch.ones(shape))\n",
    "        self.beta = nn.Parameter(torch.zeros(shape))\n",
    "        # 不参与求梯度和迭代的变量，全在内存上初始化成0\n",
    "        self.moving_mean = torch.zeros(shape)\n",
    "        self.moving_var = torch.zeros(shape)\n",
    "    def forward(self, X):\n",
    "        # 如果X不在内存上，将moving_mean和moving_var复制到X所在显存上\n",
    "        if self.moving_mean.device != X.device:\n",
    "            self.moving_mean = self.moving_mean.to(X.device)\n",
    "            self.moving_var = self.moving_var.to(X.device)\n",
    "        # 保存更新过的moving_mean和moving_var, Module实例的traning属性默认为true, 调用.eval()后设成false\n",
    "        Y, self.moving_mean, self.moving_var = batch_norm(self.training, X, self.gamma, self.beta, self.moving_mean, self.moving_var, eps=1e-6, momentum=0.9)\n",
    "        return Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用批量归一化层的LeNet\n",
    "修改之前的LeNet模型，从而应用批量归一化层。在所有的卷积层或全连接层之后，激活函数之前加入批量归一化层。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (0): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (1): BatchNorm()\n",
      "  (2): Sigmoid()\n",
      "  (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (4): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (5): BatchNorm()\n",
      "  (6): Sigmoid()\n",
      "  (7): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (8): FlattenLayer()\n",
      "  (9): Linear(in_features=256, out_features=120, bias=True)\n",
      "  (10): BatchNorm()\n",
      "  (11): Sigmoid()\n",
      "  (12): Linear(in_features=120, out_features=84, bias=True)\n",
      "  (13): BatchNorm()\n",
      "  (14): Sigmoid()\n",
      "  (15): Linear(in_features=84, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "class FlattenLayer(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(FlattenLayer, self).__init__()\n",
    "    def forward(self, x):\n",
    "        return x.view(x.shape[0], -1)\n",
    "\n",
    "net = nn.Sequential(\n",
    "    nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5),\n",
    "    BatchNorm(num_features=6, num_dims=4),\n",
    "    nn.Sigmoid(),\n",
    "    nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "    nn.Conv2d(6, 16, 5),\n",
    "    BatchNorm(16, 4),\n",
    "    nn.Sigmoid(),\n",
    "    nn.MaxPool2d(2, 2),\n",
    "    FlattenLayer(),\n",
    "    nn.Linear(16*4*4, 120),\n",
    "    BatchNorm(120, num_dims=2),\n",
    "    nn.Sigmoid(),\n",
    "    nn.Linear(120, 84),\n",
    "    BatchNorm(84, num_dims=2),\n",
    "    nn.Sigmoid(),\n",
    "    nn.Linear(84, 10)\n",
    ")\n",
    "\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 output shape:  torch.Size([256, 6, 24, 24])\n",
      "1 output shape:  torch.Size([256, 6, 24, 24])\n",
      "2 output shape:  torch.Size([256, 6, 24, 24])\n",
      "3 output shape:  torch.Size([256, 6, 12, 12])\n",
      "4 output shape:  torch.Size([256, 16, 8, 8])\n",
      "5 output shape:  torch.Size([256, 16, 8, 8])\n",
      "6 output shape:  torch.Size([256, 16, 8, 8])\n",
      "7 output shape:  torch.Size([256, 16, 4, 4])\n",
      "8 output shape:  torch.Size([256, 256])\n",
      "9 output shape:  torch.Size([256, 120])\n",
      "10 output shape:  torch.Size([256, 120])\n",
      "11 output shape:  torch.Size([256, 120])\n",
      "12 output shape:  torch.Size([256, 84])\n",
      "13 output shape:  torch.Size([256, 84])\n",
      "14 output shape:  torch.Size([256, 84])\n",
      "15 output shape:  torch.Size([256, 10])\n"
     ]
    }
   ],
   "source": [
    "# 构建一个批量的数据样本来查看每个模块的输出形状\n",
    "x = torch.rand(256, 1, 28, 28)\n",
    "for name,blk in net.named_children():\n",
    "    x = blk(x)\n",
    "    print(name, 'output shape: ', x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training on  cuda\n",
      "epoch 1, loss 0.9890, train_acc 0.785, test_acc 0.796, time 13.0 sec\n",
      "epoch 2, loss 0.4585, train_acc 0.864, test_acc 0.838, time 10.9 sec\n",
      "epoch 3, loss 0.3682, train_acc 0.878, test_acc 0.852, time 11.6 sec\n",
      "epoch 4, loss 0.3338, train_acc 0.886, test_acc 0.867, time 11.2 sec\n",
      "epoch 5, loss 0.3100, train_acc 0.892, test_acc 0.857, time 10.6 sec\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "def load_data_fashion_mnist(batch_size, resize=None, root=\"Datasets/FashionMNIST\"):\n",
    "    \"\"\"Download the fashion mnist dataset and then load into memory.\"\"\"\n",
    "    trans = []\n",
    "    if resize:\n",
    "        trans.append(transforms.Resize(size=resize))\n",
    "    trans.append(transforms.ToTensor())\n",
    "    transform = transforms.Compose(trans)\n",
    "    mnist_train = torchvision.datasets.FashionMNIST(root=root, train=True, download=True, transform=transform)\n",
    "    mnist_test = torchvision.datasets.FashionMNIST(root=root, train=False, download=True, transform=transform)\n",
    "    \n",
    "    if sys.platform.startswith('win'):\n",
    "        num_workers = 4\n",
    "    else:\n",
    "        num_workers = 4\n",
    "    train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=4)\n",
    "    test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=4)\n",
    "    return train_iter, test_iter\n",
    "\n",
    "def evaluate_accuracy(data_iter, net, device=None):\n",
    "    if device is None and isinstance(net, torch.nn.Module):\n",
    "        # 如果没有指定device，就使用net的device\n",
    "        device = list(net.parameters())[0].device\n",
    "    acc_sum, n = 0.0, 0\n",
    "    with torch.no_grad():\n",
    "        for X, y in data_iter:\n",
    "            if isinstance(net, torch.nn.Module):\n",
    "                net.eval() # 评估模型，这会关闭dropout\n",
    "                acc_sum += (net(X.to(device)).argmax(dim=1)==y.to(device)).float().sum().cpu().item()\n",
    "                net.train() # 改回训练模型\n",
    "            else: # 自定义的模型，不考虑使用GPU\n",
    "                if ('is_training' in net.__code__.co_varnames): # 如果有is_training这个参数\n",
    "                    # 将is_training设置成False\n",
    "                    acc_sum += (net(x, is_training=False).argmax(dim=1)==y).float().sum().item()\n",
    "                else:\n",
    "                    acc_sum += (net(x).argmax(dim=1)==y).float().sum().item()\n",
    "            n += y.shape[0]\n",
    "    return acc_sum/n\n",
    "\n",
    "def train_model(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs):\n",
    "    net = net.to(device)\n",
    "    print('training on ', device)\n",
    "    loss = torch.nn.CrossEntropyLoss()\n",
    "    for epoch in range(num_epochs):\n",
    "        train_ls_sum, train_acc_sum = 0.0, 0.0\n",
    "        n, batch_count, start = 0, 0, time.time()\n",
    "        for X, y in train_iter:\n",
    "            X = X.to(device)\n",
    "            y = y.to(device)\n",
    "            y_hat = net(X)\n",
    "            l = loss(y_hat, y)\n",
    "            optimizer.zero_grad()\n",
    "            l.backward()\n",
    "            optimizer.step()\n",
    "            train_ls_sum += l.cpu().item()\n",
    "            train_acc_sum += (y_hat.argmax(dim=1)==y).sum().cpu().item()\n",
    "            n += y.shape[0]\n",
    "            batch_count += 1\n",
    "        test_acc = evaluate_accuracy(test_iter, net)\n",
    "        print('epoch %d, loss %.4f, train_acc %.3f, test_acc %.3f, time %.1f sec' % (epoch + 1, train_ls_sum/batch_count, train_acc_sum/n, test_acc, time.time() - start))\n",
    "\n",
    "batch_size = 256\n",
    "train_iter, test_iter = load_data_fashion_mnist(batch_size)\n",
    "lr, num_epochs = 0.001, 5\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=lr)\n",
    "train_model(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([1.1987, 1.0321, 0.9234, 1.1193, 1.0237, 0.9071], device='cuda:0',\n",
       "        grad_fn=<ViewBackward>),\n",
       " tensor([ 0.0649, -0.5589, -0.0449,  0.2876, -0.5708,  0.4388], device='cuda:0',\n",
       "        grad_fn=<ViewBackward>))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看一下第一个批量归一化层学习到的拉伸参数gamma和偏移参数beta\n",
    "net[1].gamma.view((-1,)), net[1].beta.view((-1,))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用Pytorch中nn模块定义的BatchNorm1d和BatchNorm2d来简洁实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (0): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (1): BatchNorm2d(6, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (2): Sigmoid()\n",
      "  (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (4): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (5): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (6): Sigmoid()\n",
      "  (7): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (8): FlattenLayer()\n",
      "  (9): Linear(in_features=256, out_features=120, bias=True)\n",
      "  (10): BatchNorm1d(120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (11): Sigmoid()\n",
      "  (12): Linear(in_features=120, out_features=84, bias=True)\n",
      "  (13): BatchNorm1d(84, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (14): Sigmoid()\n",
      "  (15): Linear(in_features=84, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# 使用Pytorch中nn模块定义的BatchNorm1d和BatchNorm2d来简洁实现\n",
    "net = nn.Sequential(\n",
    "    nn.Conv2d(1, 6, 5),\n",
    "    nn.BatchNorm2d(num_features=6), # 如果是接在卷积层的后面，则看成是输出通道数\n",
    "    nn.Sigmoid(),\n",
    "    nn.MaxPool2d(2, 2),\n",
    "    nn.Conv2d(6, 16, 5),\n",
    "    nn.BatchNorm2d(16),\n",
    "    nn.Sigmoid(),\n",
    "    nn.MaxPool2d(2, 2),\n",
    "    FlattenLayer(),\n",
    "    nn.Linear(16*4*4, 120),\n",
    "    nn.BatchNorm1d(120), # 如果是接在全连接层后面，则看成是特征个数（维数）\n",
    "    nn.Sigmoid(),\n",
    "    nn.Linear(120, 84),\n",
    "    nn.BatchNorm1d(84),\n",
    "    nn.Sigmoid(),\n",
    "    nn.Linear(84, 10)\n",
    ")\n",
    "\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 output shape:  torch.Size([256, 6, 24, 24])\n",
      "1 output shape:  torch.Size([256, 6, 24, 24])\n",
      "2 output shape:  torch.Size([256, 6, 24, 24])\n",
      "3 output shape:  torch.Size([256, 6, 12, 12])\n",
      "4 output shape:  torch.Size([256, 16, 8, 8])\n",
      "5 output shape:  torch.Size([256, 16, 8, 8])\n",
      "6 output shape:  torch.Size([256, 16, 8, 8])\n",
      "7 output shape:  torch.Size([256, 16, 4, 4])\n",
      "8 output shape:  torch.Size([256, 256])\n",
      "9 output shape:  torch.Size([256, 120])\n",
      "10 output shape:  torch.Size([256, 120])\n",
      "11 output shape:  torch.Size([256, 120])\n",
      "12 output shape:  torch.Size([256, 84])\n",
      "13 output shape:  torch.Size([256, 84])\n",
      "14 output shape:  torch.Size([256, 84])\n",
      "15 output shape:  torch.Size([256, 10])\n"
     ]
    }
   ],
   "source": [
    "# 构建一个批量的数据样本来查看每个模块的输出形状\n",
    "x = torch.rand(256, 1, 28, 28)\n",
    "for name,blk in net.named_children():\n",
    "    x = blk(x)\n",
    "    print(name, 'output shape: ', x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training on  cuda\n",
      "epoch 1, loss 0.9939, train_acc 0.791, test_acc 0.838, time 8.4 sec\n",
      "epoch 2, loss 0.4530, train_acc 0.866, test_acc 0.817, time 8.0 sec\n",
      "epoch 3, loss 0.3610, train_acc 0.881, test_acc 0.858, time 7.6 sec\n",
      "epoch 4, loss 0.3262, train_acc 0.889, test_acc 0.849, time 7.8 sec\n",
      "epoch 5, loss 0.3024, train_acc 0.896, test_acc 0.857, time 8.4 sec\n"
     ]
    }
   ],
   "source": [
    "# 使用同样的超参数进行训练\n",
    "batch_size = 256\n",
    "train_iter, test_iter = load_data_fashion_mnist(batch_size)\n",
    "lr, num_epochs = 0.001, 5\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=lr)\n",
    "train_model(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 小结\n",
    "1、在模型训练时，批量归一化利用小批量上的均值和标准差，不断调整神经网络的中间输出，从而使整个神经网络在各层的中间输出的数值更稳定。\n",
    "\n",
    "2、对全连接层和卷积层做批量归一化的方法稍有不同。\n",
    "\n",
    "3、批量归一化层和丢弃层一样，在训练模式和预测模式的计算结果是不一样的。\n",
    "\n",
    "4、PyTorch提供了BatchNorm类方便使用。"
   ]
  }
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